{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/15438"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/15438","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"The measurement of flow velocity distribution","abstract":"A method for the improvement of the range and accuracy achieved by the cross-correlation flowmeter is investigated. The principles of the flowmeter operation and fundamental digital signal processing techniques are reviewed. The process of the Fourier transform deconvolution is investigated. Computer simulation of the flow system is described and is shown to require impractical amounts of computer time to achieve the necessary averaging times. Consequently, correlation and velocity profile measurements are made from an experimental flow rig. A waveform analysis program is used to analyse these measurements. The Fourier transform deconvolution is shown in this case to have poor noise immunity. For this reason, an alternative method of Bayesian deconvolution is investigated. The correlation functions measured from the experimental flow rig are deconvolved using the Bayesian deconvolution algorithm. The resulting transit time distribution is shown to converge to the transit time distribution obtained from the velocity profile measurements. From an analysis of the flow signals the velocity distribution of the flow may thus be found.","abstract_html":"A method for the improvement of the range and accuracy achieved by the cross-correlation flowmeter is investigated. The principles of the flowmeter operation and fundamental digital signal processing techniques are reviewed. The process of the Fourier transform deconvolution is investigated. Computer simulation of the flow system is described and is shown to require impractical amounts of computer time to achieve the necessary averaging times. Consequently, correlation and velocity profile measurements are made from an experimental flow rig. A waveform analysis program is used to analyse these measurements. The Fourier transform deconvolution is shown in this case to have poor noise immunity. For this reason, an alternative method of Bayesian deconvolution is investigated. The correlation functions measured from the experimental flow rig are deconvolved using the Bayesian deconvolution algorithm. The resulting transit time distribution is shown to converge to the transit time distribution obtained from the velocity profile measurements. From an analysis of the flow signals the velocity distribution of the flow may thus be found.","abstract_has_math":false,"creators":["Dann, Michael Stephen"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Greene, J R"],"committee_chairs":[],"committee_members":[],"year":1981,"date_issued":"1981","date_published":"1981","updated_at":"2026-07-22T22:23:04Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/15438","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Greene, J R"]},{"key":"dc:creator","label":"Author","values":["Dann, Michael Stephen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-11-30T08:23:45Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-11-30T08:23:45Z"]},{"key":"dc:date.issued","label":"Date","values":["1981"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc (Eng)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/15438"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Includes bibliography."]},{"key":"dc:description.abstract","label":"Abstract","values":["A method for the improvement of the range and accuracy achieved by the cross-correlation flowmeter is investigated. The principles of the flowmeter operation and fundamental digital signal processing techniques are reviewed. The process of the Fourier transform deconvolution is investigated. Computer simulation of the flow system is described and is shown to require impractical amounts of computer time to achieve the necessary averaging times. Consequently, correlation and velocity profile measurements are made from an experimental flow rig. A waveform analysis program is used to analyse these measurements. The Fourier transform deconvolution is shown in this case to have poor noise immunity. For this reason, an alternative method of Bayesian deconvolution is investigated. The correlation functions measured from the experimental flow rig are deconvolved using the Bayesian deconvolution algorithm. The resulting transit time distribution is shown to converge to the transit time distribution obtained from the velocity profile measurements. From an analysis of the flow signals the velocity distribution of the flow may thus be found."]},{"key":"dc:title","label":"Title","values":["The measurement of flow velocity distribution"]}]}],"canonical_facts":{"dc:contributor.advisor":["Greene, J R"],"dc:creator":["Dann, Michael Stephen"],"dc:date.accessioned":["2015-11-30T08:23:45Z"],"dc:date.available":["2015-11-30T08:23:45Z"],"dc:date.issued":["1981"],"dc:description":["Includes bibliography."],"dc:description.abstract":["A method for the improvement of the range and accuracy achieved by the cross-correlation flowmeter is investigated. The principles of the flowmeter operation and fundamental digital signal processing techniques are reviewed. The process of the Fourier transform deconvolution is investigated. Computer simulation of the flow system is described and is shown to require impractical amounts of computer time to achieve the necessary averaging times. Consequently, correlation and velocity profile measurements are made from an experimental flow rig. A waveform analysis program is used to analyse these measurements. The Fourier transform deconvolution is shown in this case to have poor noise immunity. For this reason, an alternative method of Bayesian deconvolution is investigated. The correlation functions measured from the experimental flow rig are deconvolved using the Bayesian deconvolution algorithm. The resulting transit time distribution is shown to converge to the transit time distribution obtained from the velocity profile measurements. 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